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Vacant Parking Slot Availability Detection Using Yolov8 and OpenCV - Integration with payment functionalities 7

Vacant Parking Slot Availability Detection Using Yolov8 and ...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Abhigna, Arsam Sreeja, Challa Mahesh, Albevar Varma, Abhirama Phani Subhahan, D. Abdus B V Raju Institute of Technology Department of Computer Science and Engineering Telangana State 502313 India

ISBN: (纸本)9798350372816

Mitigating urban traffic and parking challenges is pivotal for residents' well-being, conserving time, energy, and patience. Employing computer vision techniques, the focus is on optimizing parking space management by evaluating slot occupancy. Instant cameras capture comprehensive parking lot data, system employing YOLOv8 and OpenCV enhances real-time parking management. It accurately identifies available and occupied parking spaces, streamlines video preprocessing, facilitates payment processes, and generates alerts, ultimately optimizing parking operations and resource utilization efficiently. This endeavor underscores a commitment to tackling genuine urban issues, reducing the time spent searching for parking and enhancing the overall driving experience. Supported by an extensive literature review on smart parking, it outlines methodologies and ecosystems to effectively address contemporary parking dilemmas. Smart parking solutions are imperative amidst escalating urbanization and traffic congestion, offering benefits not only in research but also in bolstering economic vitality. © 2024 IEEE.

关键词： Traffic congestion

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Sentiment Analysis in Gujarati Language with Dictionary Approach 6th

Sentiment Analysis in Gujarati Language with Dictionary Ap...

引用

6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Dudhia, Devanshu J. Rana, Dipti Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat395007 India

ISBN: (纸本)9783031707889

In recent years, due to the availability of voluminous data on the web for Indian languages, it has become an important task to analyze this data to retrieve useful information. Because of the growth of Indian language content, it is beneficial to utilize this explosion of data. In this research, the deliberate choice of Gujarati language for sentiment analysis stems from a commitment to capture the distinct linguistic and cultural characteristics of the Gujarat region. For the purpose of sentiment analysis, created a Gujarati tweets dataset by crawling on particular hashtags. Once the dataset is prepared, performed all necessary pre-processing steps on our Gujarati dataset of tweets. After that, perform the sentiment analysis using a dictionary approach. In this approach, created dictionaries of positive and negative words manually and also took the help of some research work as well. With the help of dictionaries, prepared the training dataset and testing dataset and apply different classification algorithms on it. Through this survey, we get to know different methods used for sentiment analysis as it is used to determine the sentiment or emotion expressed in a piece of text, whether positive or negative. Also, get to know how to count the sentiment score of Gujarati tweets. Additionally, we explore the significance of our dataset and its potential for fostering future research endeavors in Gujarati language analysis. This comprehensive analysis contributes to a deeper understanding of sentiment dynamics in Gujarati, with implications for diverse applications in natural language processing and cultural studies. There are many potential applications for sentiment analysis such as marketing and customer service, social media monitoring, political analysis, public health, financial analysis, disaster response, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Tweets

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Noise Pollution Detection and Notification Using Flet and Arduino

Noise Pollution Detection and Notification Using Flet and Ar...

引用

2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Kala, I. Swathy, S. Vishnu Priya, V. Harshavarthan, P.K. PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350378177

This project seeks to identify urban, traffic, and industrial noise hazards. This is accomplished by Arduino, which uses sound sensors to recognize and differentiate between light and loud noise. With the help of the ThingSpeak server, the noise level data for different time is also tracked. The proj ect extends its benefits to the users by notifying users via mail if they are in an area with high noise levels. The Flet User Interface framework was used in the development of this web application. © 2024 IEEE.

关键词： Background noise

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Smart Crop Recommender System Using Machine Learning 2

Smart Crop Recommender System Using Machine Learning

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Nokshangthemba Kumar, Saurabh Soloman, Shyam Sunder Jannu Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350366570

Agriculture is key to any country's economic growth. However, meeting food requirements of the present population due to the ever-rising human population, changing climatic conditions, and limited natural resources becomes a difficult problem. Smart agriculture is an innovative way of supporting farming by recommending the best crops to use and solving the current problems in maintaining farming sustainable. The classification machine learning algorithm is used to build the recommendation model. This paper's concept is a method of choosing appropriate crops that results in several favorable outcomes for agriculture and farmers. Since the crop yield can be increased, it will contribute to boosting our country's economy. Crops recommendation system for agriculture is based on various attributes such as crop type, soil composition, weather condition, soil pH and rainfall. The data have taken from the historic data which was collected from soil and water conservation and district agriculture office Nagaland and use supervised machine learning approaches to apply different ML algorithms. The effected of algorithm are measure on a various scale including Accuracy, Precision, Recall and F1-Score. The random forest outperformed in specifying the suitable crop with an impressive accuracy of 92.59%. © 2024 IEEE.

关键词： Smart agriculture

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WDRKNN: Weighted Distance Representation based Nearest Neighbor for Hyperspectral Remote Sensing Imagery Classification 14

WDRKNN: Weighted Distance Representation based Nearest Neigh...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Monika Biswas, Mantosh National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India University of Delhi Department of Computer Science Delhi India

ISBN: (纸本)9798350335095

Hyperspectral image (HSI) classification has enormous importance in the era of remote sensing and extensively sourced in a variety of promising applications. Most HSI classifications use the K-nearest neighbor (KNN) method due to its efficiency and simplicity. Although the KNN-based classification has lot of benefits, while this classification method has two big issues: firstly, the sensitivity of the k-neighborhoods affects KNN performance and secondly, moderate majority voting utilized there especially if the size of samples is quite small. To tackle these outliers, this paper suggests the weighted distance representation-based K nearest neighbor (WDRKNN) approach for improving the image classification results. In proposed WDRKNN, initially for each pattern class k nearest neighbors belongs to testing data samples are chosen and it has been employed to imply the testing data sample as their linear combinations. The localities related to the k-nearest neighbors for each pattern class represent as the weighted distance restrict their subsequent representations related constants. Decisively, the testing sample will be classified to a specific pattern class by the WDRKNN using the least representation-based distance between the classes. Experimental assessment on two HSIs: Pavia University and Indian Pines confirms that the suggested structure can significantly enhance HSI classification as compared to related state-of-the-art methodologies. © 2023 IEEE.

关键词： Remote sensing

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Efficient Skin Lesion based Classification System for Monkeypox Detection using VGG16 and Ensemble Learning 10

Efficient Skin Lesion based Classification System for Monkey...

引用

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Pabbi, Varinder Khullar, Vikas Angurala, Mohit Ramgarhia Institute of Engineering and Technology Department of Computer Applications Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789380544472

Monkeypox is a recently emerged disease outbreak that has impacted the health of many individuals. This study focuses on developing a skin lesion-based classification system for monkeypox disease. Many recent works suggest that the direct implementation of machine learning (ML) and deep learning (DL) approaches for this task has not resulted in high accuracy. Therefore, this paper proposes an efficient technique that employs a hybrid of VGG16-based feature engineering, class balancing, and ensemble learning for the detection of monkeypox skin lesions. Specifically, DL-based feature extraction is applied, but the classification is carried out through ensemble learning techniques. The results demonstrate that the random forest, extra tree, and histogram gradient descent methods achieved better performance in terms of different performance metrics. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Feature extraction

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Enhancing Sarcasm Detection with Contextual Features Through Multi Layered Perceptron Model 2

Enhancing Sarcasm Detection with Contextual Features Through...

引用

2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Jannu Soloman, Shyam Sunder Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350366570

Sarcasm Detection is a challenging task due to its often nuanced and context-dependent nature. Sarcasm detection has emerged as a critical area in natural language processing, finding applications in sentiment analysis, social media monitoring and customer feedback analysis. The proposed model focuses on the advancing the accuracy and robustness of sarcasm detection model by integrating contextual features and employing sophisticated neural network architecture. The primary objective of the proposed work is to analyze textual data and obtain the best possible features. Features such as sarcasymbols, attenuation indicators and selective stopwords are carefully selected to enhance the model's ability to detect subtle sarcasm cues. By utilizing vector representation of textual data, extracted features and deep neural network, the proposed model identifies sarcasm from textual data. To evaluate the performance of the approaches in the proposed model, the metrics F1-Score and accuracy are used. The proposed model resulted in an impressive accuracy and F1 Score for the binary classification problem in bifurcating the text as sarcastic and non-sarcastic with 86.31 and 87.17 percentages respectively. © 2024 IEEE.

关键词： Deep neural networks

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A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning 25

A Case Study on Context-Aware Neural Machine Translation wit...

引用

25th Annual Conference of the European Association for Machine Translation, EAMT 2024

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781068690709

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German-English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024 The authors, © 2024 European Association for Machine Translation.

关键词： Neural machine translation

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Enhancing Robustness of Deep Learning Model for Lung Cancer Detection: Adversarial Training and Gradient-based Interpretability

Enhancing Robustness of Deep Learning Model for Lung Cancer ...

引用

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Srivastava, Pragya Mishra, Asha Rani Chauhan, Sansar Singh Department of Computer Science and Engineering GL Bajaj Institute of Technology and Management UP Greater Noida India

ISBN: (纸本)9798350355338

Lung cancer is a common and deadly type of cancer that is found universal. Identifying it early is crucial for improving patient results. Alternatively, it can be difficult because of the complexity of analysing radiological images. This research presents a robust deep learning model for detecting cancerous lesions in chest CT scan images. The model, using the ResNet50 architecture, experience training and evaluation using a dataset comprising interpreted chest CT scans obtained from Kaggle. The dataset was split into training, validation, and test sets, ensuring an equal distribution of cancerous and non-cancerous scans. After comprehensive training and validation, the model achieved an impressive accuracy of 99.68% in distinguishing between cancerous and non-cancerous images. Furthermore, the utilization of Grad-CAM visualization techniques has augmented the interpretability of the model, offering substantial discernment into the precise areas of interest within the CT scan images. Moreover, the model has shown flexibility in opposing adversarial attacks, thereby emphasizing its dependability in clinical environments. In summary, this study highlights the capacity of deep learning models to enhance diagnostic precision in healthcare, thereby potentially improving patient care and outcomes. © 2024 IEEE.

关键词： Lung cancer

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AI in Space 15

AI in Space

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavyashree, N. Zareen, Afshan Amrutha, J. Sharath, M.N. Department of Computer Science and Engineering BGS institute of technology Karnataka B G Nagar India Department of CSE Rajeev Institute of Technology Hassan India

ISBN: (纸本)9798331300579

Artificial intelligence (AI) and its subset, machine learning (ML), are growing in acceptance within the space *** days, autonomous navigation, spacecraft health monitoring, and operational management of satellite constellations all make extensive use of ML ***, a lot of research on AI applications in space missions can be categorized into two main *** disadvantage of the first category is that it is out of date and ignores a number of noteworthy and recent advances in the field, such as the contributions of deep learning (DL) and bioinspired AI *** issue with the second category is that it describes specific AI techniques and algorithms in excessive depth, which might be *** to these limitations, a concise. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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